1. Technical Field
The invention is related to a system for tracking patterns, and in particular, to a system and method for using probabilistic techniques to track patterns with exemplars generated from training data.
2. Related Art
There are many existing schemes for tracking objects. One class of object tracking schemes uses systems that are driven either by image features or by raw image intensity, or some combination thereof. Either way, the tracking problem can be formulated in a probabilistic framework in either or both feature-driven or intensity-driven tracking schemes. One clear advantage to using a probabilistic framework for tracking is that tracking uncertainty is handled in a systematic fashion, using both sensor fusion and temporal fusion. Such schemes are often quite successful in tracking objects. However, many such tracking schemes require the use of complex models having parameters that roughly represent an object that is being tracked in combination with one or more tracking functions. As a result, such schemes suffer from a common problem, namely, the expense, time, and difficulty in defining and training the models for each object class that is to be tracked.
Consequently, to address the problem of complicated and costly object models, another class of tracking schemes has been developed. This new class of tracking schemes provides an alternative to the use of object models and tracking functions by making use of “exemplars” for tracking objects. Exemplar-based models are typically constructed directly from training sets using conventional techniques, without the need to set up complex intermediate representations such as parameterized contour models or 3-D articulated models.
Unfortunately, existing tracking schemes that use exemplar-based models have certain limitations. For example, one fairly effective exemplar-based tracking scheme, referred to as “single-frame exemplar-based tracking,” is limited by its inability to incorporate temporal constraints. Consequently, this scheme tends to produce jerky recovered motion. Further, the inability to incorporate temporal constraints also serves to reduce the ability to recover from occlusion or partial masking of the object being tracked.
Other conventional exemplar-based tracking schemes make use of a probabilistic frame-work to achieve full temporal tracking via Kalman filtering or particle filtering. One such scheme embeds exemplars in learned probabilistic models by treating them as centers in probabilistic mixtures. This scheme uses fully automated motion-sequence analysis, requiring only the structural form of a generative image-sequence model to be specified in advance. However, this approach also has several limitations.
In particular, the aforementioned scheme uses online expectation-maximization (EM) for probabilistic inference. Unfortunately, EM is both computationally intensive and limited, for practical purposes, to low resolution images. Another drawback to this approach is that images representing objects to be tracked must be represented as simple arrays of pixels. As a result, this scheme can not make use of nonlinear transformations that could help with invariance to scene conditions, such as, for example, conversion of images to edge maps. Still another drawback of this scheme is that image noise is treated as white noise, even where there are known, strong statistical correlations between image pixels. Consequently, otherwise valuable information is simply ignored, this reducing the tracking effectiveness of this scheme. Finally, because the exemplars in this scheme lack a vector-space structure, conventional probabilistic treatments, such as is useful for tracking schemes using object models as described above, are not used with this scheme.
Therefore, what is needed is a system and method for reliably tracking target objects or patterns without the need to use complex representations or explicit models of the objects or patterns being tracked. Thus, such a system and method should make use of exemplars rather than models. Further, such a system and method should make use a probabilistic treatment of the exemplars in order to better deal with uncertainty in tracking the objects or patterns.